






"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor

def _a					(						_snake_case				):







      """simple docstring"""






      UpperCAmelCase							     =			SwinaSRConfig()

      if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            UpperCAmelCase							     =			4
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            UpperCAmelCase							     =			4
            UpperCAmelCase							     =			48
            UpperCAmelCase							     =			"""pixelshuffle_aux"""
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            UpperCAmelCase							     =			[6, 6, 6, 6]
            UpperCAmelCase							     =			60
            UpperCAmelCase							     =			[6, 6, 6, 6]
            UpperCAmelCase							     =			"""pixelshuffledirect"""
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            UpperCAmelCase							     =			4
            UpperCAmelCase							     =			"""nearest+conv"""
      elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
            UpperCAmelCase							     =			1
            UpperCAmelCase							     =			1
            UpperCAmelCase							     =			126
            UpperCAmelCase							     =			7
            UpperCAmelCase							     =			255.0
            UpperCAmelCase							     =			""""""

      return config

def _a					(						_snake_case ,			_snake_case				):







      """simple docstring"""






      if "patch_embed.proj" in name and "layers" not in name:
            UpperCAmelCase							     =			name.replace("""patch_embed.proj""" ,			"""embeddings.patch_embeddings.projection"""				)
      if "patch_embed.norm" in name:
            UpperCAmelCase							     =			name.replace("""patch_embed.norm""" ,			"""embeddings.patch_embeddings.layernorm"""				)
      if "layers" in name:
            UpperCAmelCase							     =			name.replace("""layers""" ,			"""encoder.stages"""				)
      if "residual_group.blocks" in name:
            UpperCAmelCase							     =			name.replace("""residual_group.blocks""" ,			"""layers"""				)
      if "attn.proj" in name:
            UpperCAmelCase							     =			name.replace("""attn.proj""" ,			"""attention.output.dense"""				)
      if "attn" in name:
            UpperCAmelCase							     =			name.replace("""attn""" ,			"""attention.self"""				)
      if "norm1" in name:
            UpperCAmelCase							     =			name.replace("""norm1""" ,			"""layernorm_before"""				)
      if "norm2" in name:
            UpperCAmelCase							     =			name.replace("""norm2""" ,			"""layernorm_after"""				)
      if "mlp.fc1" in name:
            UpperCAmelCase							     =			name.replace("""mlp.fc1""" ,			"""intermediate.dense"""				)
      if "mlp.fc2" in name:
            UpperCAmelCase							     =			name.replace("""mlp.fc2""" ,			"""output.dense"""				)
      if "q_bias" in name:
            UpperCAmelCase							     =			name.replace("""q_bias""" ,			"""query.bias"""				)
      if "k_bias" in name:
            UpperCAmelCase							     =			name.replace("""k_bias""" ,			"""key.bias"""				)
      if "v_bias" in name:
            UpperCAmelCase							     =			name.replace("""v_bias""" ,			"""value.bias"""				)
      if "cpb_mlp" in name:
            UpperCAmelCase							     =			name.replace("""cpb_mlp""" ,			"""continuous_position_bias_mlp"""				)
      if "patch_embed.proj" in name:
            UpperCAmelCase							     =			name.replace("""patch_embed.proj""" ,			"""patch_embed.projection"""				)

      if name == "norm.weight":
            UpperCAmelCase							     =			"""layernorm.weight"""
      if name == "norm.bias":
            UpperCAmelCase							     =			"""layernorm.bias"""

      if "conv_first" in name:
            UpperCAmelCase							     =			name.replace("""conv_first""" ,			"""first_convolution"""				)

      if (
          "upsample" in name
          or "conv_before_upsample" in name
          or "conv_bicubic" in name
          or "conv_up" in name
          or "conv_hr" in name
          or "conv_last" in name
          or "aux" in name
      ):
            # heads
            if "conv_last" in name:
                  UpperCAmelCase							     =			name.replace("""conv_last""" ,			"""final_convolution"""				)
            if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
                  if "conv_before_upsample.0" in name:
                        UpperCAmelCase							     =			name.replace("""conv_before_upsample.0""" ,			"""conv_before_upsample"""				)
                  if "upsample.0" in name:
                        UpperCAmelCase							     =			name.replace("""upsample.0""" ,			"""upsample.convolution_0"""				)
                  if "upsample.2" in name:
                        UpperCAmelCase							     =			name.replace("""upsample.2""" ,			"""upsample.convolution_1"""				)
                  UpperCAmelCase							     =			"""upsample.""" + name
            elif config.upsampler == "pixelshuffledirect":
                  UpperCAmelCase							     =			name.replace("""upsample.0.weight""" ,			"""upsample.conv.weight"""				)
                  UpperCAmelCase							     =			name.replace("""upsample.0.bias""" ,			"""upsample.conv.bias"""				)
            else:
                  pass
      else:
            UpperCAmelCase							     =			"""swin2sr.""" + name

      return name

def _a					(						_snake_case ,			_snake_case				):







      """simple docstring"""






      for key in orig_state_dict.copy().keys():
            UpperCAmelCase							     =			orig_state_dict.pop(__lowerCAmelCase				)

            if "qkv" in key:
                  UpperCAmelCase							     =			key.split("""."""				)
                  UpperCAmelCase							     =			int(key_split[1]				)
                  UpperCAmelCase							     =			int(key_split[4]				)
                  UpperCAmelCase							     =			config.embed_dim

                  if "weight" in key:
                        UpperCAmelCase							     =			val[:dim, :]
                        UpperCAmelCase							     =			val[dim : dim * 2, :]
                        UpperCAmelCase							     =			val[-dim:, :]
                  else:
                        UpperCAmelCase							     =			val[:dim]
                        UpperCAmelCase							     =			val[dim : dim * 2]
                        UpperCAmelCase							     =			val[-dim:]
                  pass
            else:
                  UpperCAmelCase							     =			val

      return orig_state_dict

def _a					(						_snake_case ,			_snake_case ,			_snake_case				):







      """simple docstring"""






      UpperCAmelCase							     =			get_config(__lowerCAmelCase				)
      UpperCAmelCase							     =			SwinaSRForImageSuperResolution(__lowerCAmelCase				)
      model.eval()

      UpperCAmelCase							     =			torch.hub.load_state_dict_from_url(__lowerCAmelCase ,			map_location="""cpu"""				)
      UpperCAmelCase							     =			convert_state_dict(__lowerCAmelCase ,			__lowerCAmelCase				)
      UpperCAmelCase     ,   UpperCAmelCase							     =			model.load_state_dict(__lowerCAmelCase ,			strict=__lowerCAmelCase				)

      if len(__lowerCAmelCase				) > 0:
            raise ValueError("""Missing keys when converting: {}""".format(__lowerCAmelCase				)				)
      for key in unexpected_keys:
            if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
                  raise ValueError(F'''Unexpected key {key} in state_dict'''				)

    # verify values
      UpperCAmelCase							     =			"""https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
      UpperCAmelCase							     =			Image.open(requests.get(__lowerCAmelCase ,			stream=__lowerCAmelCase				).raw				).convert("""RGB"""				)
      UpperCAmelCase							     =			SwinaSRImageProcessor()
      # pixel_values = processor(image, return_tensors="pt").pixel_values

      UpperCAmelCase							     =			126 if """Jpeg""" in checkpoint_url else 256
      UpperCAmelCase							     =			Compose(
          [
              Resize((image_size, image_size)				),
              ToTensor(),
              Normalize(mean=[0.485, 0.456, 0.406] ,			std=[0.229, 0.224, 0.225]				),
          ]				)
      UpperCAmelCase							     =			transforms(__lowerCAmelCase				).unsqueeze(0				)

      if config.num_channels == 1:
            UpperCAmelCase							     =			pixel_values[:, 0, :, :].unsqueeze(1				)

      UpperCAmelCase							     =			model(__lowerCAmelCase				)

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            UpperCAmelCase							     =			torch.Size([1, 3, 512, 512]				)
            UpperCAmelCase							     =			torch.tensor(
                [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]				)
      elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            UpperCAmelCase							     =			torch.Size([1, 3, 1024, 1024]				)
            UpperCAmelCase							     =			torch.tensor(
                [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]				)
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            # TODO values didn't match exactly here
            UpperCAmelCase							     =			torch.Size([1, 3, 1024, 1024]				)
            UpperCAmelCase							     =			torch.tensor(
                [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]				)
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            UpperCAmelCase							     =			torch.Size([1, 3, 512, 512]				)
            UpperCAmelCase							     =			torch.tensor(
                [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]				)
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            UpperCAmelCase							     =			torch.Size([1, 3, 1024, 1024]				)
            UpperCAmelCase							     =			torch.tensor(
                [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]				)

      assert (
          outputs.reconstruction.shape == expected_shape
      ), F'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
      assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] ,			__lowerCAmelCase ,			atol=1E-3				)
      print("""Looks ok!"""				)

      UpperCAmelCase							     =			{
          """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
              """swin2SR-classical-sr-x2-64"""
          ),
          """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
              """swin2SR-classical-sr-x4-64"""
          ),
          """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
              """swin2SR-compressed-sr-x4-48"""
          ),
          """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
              """swin2SR-lightweight-x2-64"""
          ),
          """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
              """swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
          ),
      }
      UpperCAmelCase							     =			url_to_name[checkpoint_url]

      if pytorch_dump_folder_path is not None:
            print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
            model.save_pretrained(__lowerCAmelCase				)
            print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
            processor.save_pretrained(__lowerCAmelCase				)

      if push_to_hub:
            model.push_to_hub(F'''caidas/{model_name}'''				)
            processor.push_to_hub(F'''caidas/{model_name}'''				)


if __name__ == "__main__":
  _UpperCamelCase	     =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""",
      type=str,
      help="""URL of the original Swin2SR checkpoint you\'d like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the converted model to the hub.""")

  _UpperCamelCase	     =    parser.parse_args()
  convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""


from .imports import is_rich_available


if is_rich_available():
	from rich.traceback import install

	install(show_locals=False)

else:
	raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')


